Department of Health Sciences (DISSAL), University of Genova, Via Pastore 1, 16132, Genova, Italy.
Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy.
BMC Med. 2019 Jun 18;17(1):113. doi: 10.1186/s12916-019-1345-2.
Personalized medicine is the tailoring of treatment to the individual characteristics of patients. Once a treatment has been tested in a clinical trial and its effect overall quantified, it would be of great value to be able to use the baseline patients' characteristics to identify patients with larger/lower benefits from treatment, for a more personalized approach to therapy.
We show here a previously published statistical method, aimed at identifying patients' profiles associated to larger treatment benefits applied to three identical randomized clinical trials in multiple sclerosis, testing laquinimod vs placebo (ALLEGRO, BRAVO, and CONCERTO). We identified on the ALLEGRO patients' specific linear combinations of baseline variables, predicting heterogeneous response to treatment on disability progression. We choose the best score on the BRAVO, based on its ability to identify responders to treatment in this dataset. We finally got an external validation on the CONCERTO, testing on this new dataset the performance of the score in defining responders and non-responders.
The best response score defined on the ALLEGRO and the BRAVO was a linear combination of age, sex, previous relapses, brain volume, and MRI lesion activity. Splitting patients into responders and non-responders according to the score distribution, in the ALLEGRO, the hazard ratio (HR) for disability progression of laquinimod vs placebo was 0.38 for responders, HR = 1.31 for non-responders (interaction p = 0.0007). In the BRAVO, we had similar results: HR = 0.40 for responders and HR = 1.24 for non-responders (interaction p = 0.006). These findings were successfully replicated in the CONCERTO study, with HR = 0.44 for responders and HR=1.08 for non-responders (interaction p = 0.033).
This study demonstrates the possibility to refine and personalize the treatment effect estimated in randomized studies by using the baseline demographic and clinical characteristics of the included patients. The method can be applied to any randomized trial in any medical condition to create a treatment-specific score associated to different levels of response to the treatment tested in the trial. This is an easy and affordable method toward therapy personalization, indicating patient profiles related to a larger benefit from a specific drug, which may have implications for taking clinical decisions in everyday clinical practice.
个性化医学是根据患者的个体特征来定制治疗方案。一旦一种治疗方法在临床试验中得到测试并对其总体效果进行量化，那么能够利用患者的基线特征来识别治疗获益更大/更小的患者，从而采用更个性化的治疗方法，将具有巨大的价值。
我们在此展示一种先前发表的统计方法，旨在识别与治疗获益更大相关的患者特征，将其应用于三项多发性硬化症的相同随机临床试验，即 laquinimod 与安慰剂的对比（ALLEGRO、BRAVO 和 CONCERTO）。我们在 ALLEGRO 中确定了患者特定的基线变量线性组合，这些组合可以预测残疾进展方面的异质性治疗反应。我们根据其在该数据集中识别治疗应答者的能力，在 BRAVO 中选择最佳评分。最后，我们在 CONCERTO 中进行了外部验证，即在该新数据集上测试评分在定义应答者和无应答者方面的性能。
在 ALLEGRO 和 BRAVO 上定义的最佳反应评分是年龄、性别、既往复发、脑容量和 MRI 病变活动的线性组合。根据评分分布将患者分为应答者和无应答者，在 ALLEGRO 中，laquinimod 与安慰剂相比残疾进展的风险比（HR）为 0.38（应答者），HR=1.31（无应答者）（交互 p=0.0007）。在 BRAVO 中，我们得到了类似的结果：HR=0.40（应答者），HR=1.24（无应答者）（交互 p=0.006）。这些发现成功地在 CONCERTO 研究中得到了复制，HR=0.44（应答者），HR=1.08（无应答者）（交互 p=0.033）。
本研究表明，通过使用纳入患者的基线人口统计学和临床特征，可以对随机研究中估计的治疗效果进行精细化和个性化处理。该方法可应用于任何医疗条件下的任何随机试验，创建与试验中测试的治疗方法不同反应水平相关的特定于治疗的评分。这是一种简单且经济实惠的个性化治疗方法，它可以指示与特定药物更大获益相关的患者特征，这可能对日常临床实践中的临床决策具有重要意义。
